When a user asks the support assistant, a Retrieval Augmented Generation (RAG) application built with LangChain to compare two products across three dimensions, they’re effectively posing six questions simultaneously. Similarity search uses a single query vector to encapsulate all the intents. The retriever then generates the best approximation of the average of those intents. The resulting answer comes back concise. The search executes without errors. The relevance scores look reasonable. Yet the retrieved chunks, while topically relevant, only cover a fraction of what the question actually asked.
In this post, we showcase a RAG application on Amazon Bedrock Managed Knowledge Base with LangChain. We run the same multi-part question through standard and agentic retrieval, and read the trace events to see the plan the model produced. We also cover what the two retrieval paths cost and when the cheaper one is the right choice.
Agentic retrieval is available on Amazon Bedrock Managed Knowledge Base. Instead of one search, Amazon Bedrock Managed Knowledge Base plans the retrieval. It breaks the question into sub-queries, runs them, judges whether it has enough evidence, and searches again if it doesn’t. The langchain-aws package exposes both agentic and standard retrieval, so you can use either from a LangChain application.
Amazon Bedrock Managed Knowledge Base , the fully managed RAG capability in Amazon Bedrock, removes the self-managed vector store, embeddings, and re-ranking models from the RAG architecture. You configure a data source, and Amazon Bedrock Managed Knowledge Bases handles chunking, embedding, storage, and retrieval. This walkthrough uses Amazon Simple Storage Service (Amazon S3) .
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